Use of the Pesticide Toxicity Index to Determine Potential Ecological Risk in the Santiago-Guadalajara River Basin, Mexico
Bibliographic record
Abstract
The Santiago-Guadalajara River Basin (SGRB), located in western Mexico, is one of the most polluted rivers in the country. A pesticide monitoring program was carried out from January 2022 to September 2022, during which time water samples collected at 25 sites in the main stem and tributaries revealed the presence of 13 of the 24 pesticides analyzed, including α-BHC, β-BHC, γ-BHC (Lindane), γ-Chlordane, Hexachlorobenzene, Heptachlor, Aldrin, α-Endosulfan, DDT, -4,4, Methoxychlor, Chlorpyrifos-methyl, Endosulfan sulfate, and Dicofol. A high level of correlation was found between the identified pesticides, which could mean that several of these pesticides reached a stable level within the monitored water bodies. Most of the identified pesticides are classified as high environmental risk according to the Stockholm Convention because of their persistence and high degree of toxicity to the environment and human health. A Pesticide Toxicity Index (PTI) was applied to identify the streams considered to be of concern due to the presence of pesticides exceeding the threshold limits established by national and international guidelines. Performing a calculation for the entire Santiago-Guadalajara River Basin, the PTI reached a value of 0.833, which, according to the criteria of this method, classifies it as a “Moderate” level of risk for aquatic life. Increased regulatory and surveillance measures by state and federal authorities are required to prevent the use of these pesticides, which have been restricted globally.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".